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The 'Newcastle Nomogram' - Statistical Modelling Predicts Malignant Transformation in Potentially Malignant Disorders.
J Oral Pathol Med. 2019 May 24;:
Authors: Goodson ML, Smith DR, Thomson PJ
Abstract
BACKGROUND: Nomograms are graphical calculating devices used to predict risk of malignant transformation (MT) or response to treatment during cancer management. To date, a nomogram has not been used to predict clinical outcome during oral potentially malignant disorder (PMD) treatment. The aim of this study was to create a nomogram for use by clinicians to predict the probability of MT, thereby facilitating accurate assessment of risk and objective decision making during individual patient management.
METHODS: Clinico-pathological data from a previously treated cohort of 590 newly presenting PMD patients were reviewed and clinical outcomes categorised as disease free, persistent PMD or MT. Multiple logistic regression was used to predict the probability of MT in the cohort using age, gender, lesion type, site, and incision biopsy histopathological diagnoses. Internal validation and calibration of the model was performed using the bootstrap method (n=1000), and bias-corrected indices of model performance were computed.
RESULTS: PMDs were predominantly leukoplakias (79%), presenting most frequently at floor of mouth and lateral tongue sites (51%); 99 patients (17%) developed oral squamous cell carcinoma (SCC) during the study period. The nomogram performed well when MT predictions were compared with patient outcome data, demonstrating good bias-corrected discrimination and calibration (Dxy = 0.58; C = 0.790), with a sensitivity of 87% and specificity 63%, and a positive predictive value of 32% and negative predictive value 96%.
CONCLUSION: The 'Newcastle Nomogram' has been developed to predict the probability of MT in PMD, based on an internally validated statistical model. Based upon readily available and patient-specific clinico-pathological data, it provides clinicians with a pragmatic diagrammatic aid for clinical decision making during diagnosis and management of PMD. This article is protected by copyright. All rights reserved.
BACKGROUND: Nomograms are graphical calculating devices used to predict risk of malignant transformation (MT) or response to treatment during cancer management. To date, a nomogram has not been used to predict clinical outcome during oral potentially malignant disorder (PMD) treatment. The aim of this study was to create a nomogram for use by clinicians to predict the probability of MT, thereby facilitating accurate assessment of risk and objective decision making during individual patient management.
METHODS: Clinico-pathological data from a previously treated cohort of 590 newly presenting PMD patients were reviewed and clinical outcomes categorised as disease free, persistent PMD or MT. Multiple logistic regression was used to predict the probability of MT in the cohort using age, gender, lesion type, site, and incision biopsy histopathological diagnoses. Internal validation and calibration of the model was performed using the bootstrap method (n=1000), and bias-corrected indices of model performance were computed.
RESULTS: PMDs were predominantly leukoplakias (79%), presenting most frequently at floor of mouth and lateral tongue sites (51%); 99 patients (17%) developed oral squamous cell carcinoma (SCC) during the study period. The nomogram performed well when MT predictions were compared with patient outcome data, demonstrating good bias-corrected discrimination and calibration (Dxy = 0.58; C = 0.790), with a sensitivity of 87% and specificity 63%, and a positive predictive value of 32% and negative predictive value 96%.
CONCLUSION: The 'Newcastle Nomogram' has been developed to predict the probability of MT in PMD, based on an internally validated statistical model. Based upon readily available and patient-specific clinico-pathological data, it provides clinicians with a pragmatic diagrammatic aid for clinical decision making during diagnosis and management of PMD. This article is protected by copyright. All rights reserved.
PMID: 31125457 [PubMed - as supplied by publisher]
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